Semantic similarity between two or more sentences
Word2vec as the name suggests will create an embedding for each word in your sentence. In order to get a sentence level embedding you would need to average (or combine in some other way) the individual embeddings together.
An example of a model to generate sentence level embedding would be the Universal Sentence Encoder (USE). You may want to try it out and see if it can outperform a word-level model in your use-case.
The original paper can be found here: https://arxiv.org/abs/1803.11175
An example blog post leveraging USE: https://medium.com/@gaurav5430/universal-sentence-encoding-7d440fd3c7c7