Yes, this superposition of patterns is quite an important aspect of SDRs and HTM. The example you give of multiple predictions is exactly right - the TM can predict a superposition of next steps. It also shows up in pooling, where a single dendritic segment may represent many different patterns. In this case the synapses represent a superposition of all these patterns.
Many of the basic properties of SDRs are preserved but it’s easier to have mix and match errors, particularly with low dimensions.
We refer to superposition in our papers as the “union” property. In  it’s in Section 3.3, from the viewpoint of a segment that has learned a bunch of patterns. The same equations apply if you have a superposition of multiple input patterns. In  it’s discussed in Section 2G & H. It’s similar to the way Bloom filters work.
The TM can preserve this property (even for pretty large unions) and carry multiple predictions forward in time, but the SP doesn’t always preserve it if you have more than a few input patterns superimposed.