No, by design, Prodigy expects you to define one label set per annotation session.
The label scheme is one of the most important parts of an NLP application and model, so allowing too much arbitrary variance during annotation can easily lead to many other problems down the line. Similarly, changing the annotation objective completely on a per task-basis really goes against Prodigy's UX philosophy and I can't think of many cases where this would be beneficial compared to an approach that uses multiple, dedicated sessions per label scheme.
That said, I understand that there are always exceptions and special use cases where you might want to do things differently. I still think doing 8 concurrent sessions makes the most sense here – especially since you do want to save the annotations to different datasets as well. Running concurrent sessions is no problem out-of-the-box and can be easily automated.
Do you have an example of one of those classes and the corresponding labels? (If you can't share the exact details, maybe you can come up with a similar-ish example?)