Is a good shuffle random state for training data really good for the model?
If this is split is a train/validation split (not a hold out test set) then you should be doing cross-validation. You are going to be overly optimistic about the performance of your model for this set of features and hyperparameters if you try to split it "just right". Cross-validation will give you a more accurate portrayal regardless of your split. If this is for a train/test split (test being a hold out test set), this is a very bad practice, since you are informing your decision on how to make the split based on the performance of the test set.