Efficient queriable data structure to represent a screen with windows on it
An easy optimization to the naive algorithm is to skip some points when you check one covered by a window. Say you scan left-to-right, top-to-bottom. If you encounter an $(x, y)$ in window $w = (l, r, w, h)$, you can jump from $x$ to $l + w + 1$ and continue. If windows are big and don't overlap, off the cuff, I'd wildly conjecture that would give you an $O(p)$ algorithm, where $p$ is the number of points in the set you return.

In fact, it could be beneficial to see whether the windows are generally wider or taller. For tall windows, it would be better to scan top-to-bottom, left-to-right; for wide windows, the method discussed above should win out. You might be able to take the idea and do scan/skip diagonally to get balanced performance across the board.

In fact, scanning left-to-right, top-to-bottom, you can remember for subsequent $h$ rows (values of $y$) that you will need to skip at the same $x$ value to the same $l + w + 1$ value, if you reach the $x$ (you might not if there's another window that overlaps). This would make top-to-bottom, left-to-right and diagonal scanning unnecessary to get equivalently good performance.

More generally, you now have two data structures: one with $O(1)$ preprocessing/construction overhead and $O(w)$ lookups, and one with $O(p)$ (possibly; maybe you can do better, or maybe my optimization doesn't really achieve this) preprocessing/construction and possibly $O(1)$ (hash/lookup table) or $O(\log p)$ (BST). So there are already two alternatives, both of which are pretty good, really...