There are high throughput DFT projects (e.g., the materials project) but (1) the calculations are very computationally expensive and (2) the search space is really large. People are doing cool stuff with ML and generative models, but it’s a pretty open research area still

Also, at the end of the day, DFT is still an imperfect approximate model. Relative trends are generally more reliable than exact correspondence with experiment, and it can have system-specific systematic errors that are hard to account for in a high throughput setting

Crystal structure prediction by itself is a very hard problem. Just given the chemical formula and asking "what is the most stable structure" is a global optimization problem. And this is just for one single composition. It isn't an easy global optimization problem either. You not only have to determine the unit cell vectors, the positions of the atoms in the unit cell, but also the number of atoms in the unit cell needed to represent the structure! Just because you are even given "Pb9Cu(PO4)6O" as the formula finding the most stable structure, let alone the superconducting non-minimum energy state is a huge undertaking. Now expand that to all of chemical space and you can see that it is not that easy!

Edit: Also look at how long these (short pre-print) DFT articles are. These aren't simple calculations to interpret.

If you run a bunch of simulations to brute-force-find what you're looking for, you run into a problem of infinitely many things to simulate. It is better to use domain knowledge to eliminate things that don't work and narrow down things that would have high chance of working in theory to give some sort of directional guidance for your research.