There are no “good” and “bad” seeds.
As explained by others before, your model or whole training procedure might not be very stable, so that some random numbers yield to good results, while others make the model collapse.
You can mostly avoid these effects in supervised learning by using appropriate parameter initialization methods.
If you are working on a new method and would like to write the results in a paper, I would recommend to use a few random seeds and report the number or successful runs as well as crashes.
On the other hand if you are explicitly using a “good” seed, I would consider this as a hack, which makes it hard to draw any useful conclusions from your method.