Why random sample from replay for DQN?
The de-correlation effect is more important than following sequence of trajectories in this case.

Single step Q-learning does not rely on trajectories to learn. It is slightly less efficient to do this in TD learning - a Q($\lambda$) algorithm which averages over multiple trajectory lengths would maybe work better if it were not for the instability of using function approximators.

Instead, the DQN-based learning bootstraps across single steps (State, Action, Reward, Next State). It doesn't need longer trajectories. And in fact due to bias caused by correlation, the neural network might suffer for it if you tried. Even with experience replay, the bootstrapping - using one set of estimates to refine another - can be unstable. So other stabilising influences are beneficial too, such as using a frozen copy of the network to estimate the TD target $R + \text{max}_{a'} Q(S', a')$ - sometimes written $R + \text{max}_{a'} \hat{q}(S', a', \theta^{\bar{ }})$ where $\theta$ are the learnable parameters for $\hat{q}$ function.

It might still be possible to use longer trajectories, sampled randomly, to get a TD target estimate based on more steps. This can be beneficial for reducing bias from bootstrapping, at the expense of adding variance due to sampling from larger space of possible trajectories (and "losing" parts of trajectories or altering predicted reward because of exploratory actions). However, the single-step method presented by DQN has shown success, and it is not clear which problems would benefit from longer trajectories. You might like to experiment with options though . . . it is not an open-and-shut case, and since the DQN paper, various other refinements have been published.